Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being the two most common types. The purpose of this work was to create an efficient homogeneous ensemble of ensembles in conjunction with numerous feature-selection methodologies for the improved detection of thyroid disorder. The dataset employed is based on real-time thyroid information obtained from the District Head Quarter (DHQ) teaching hospital, Dera Ghazi (DG) Khan, Pakistan. Following the necessary preprocessing steps, three types of attribute-selection strategies; Select From Model (SFM), Select K-Best (SKB), and Recursive Feature Elimination (RFE) were used. Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and Random Forest (RF) classifiers were used as promising feature estimators. The homogeneous ensembling activated the bagging- and boosting-based classifiers, which were then classified by the Voting ensemble using both soft and hard voting. Accuracy, sensitivity, mean square error, hamming loss, and other performance assessment metrics have been adopted. The experimental results indicate the optimum applicability of the proposed strategy for improved thyroid ailment identification. All of the employed approaches achieved 100% accuracy with a small feature set. In terms of accuracy and computational cost, the presented findings outperformed similar benchmark models in its domain.

Energy-saving strategies cannot be implemented without having detailed and regular power consumption data of a facility. The installation of an energy monitoring and data logging system can help in planning energy efficiency improvement policies by providing daily, monthly, and yearly energy consumption reports and graphs. The purpose of this study was to demonstrate the impact of an energy monitoring and management system on the improvement of energy efficiency in the industrial sector of developing countries. This study introduced an energy monitoring and data logging system installed in an automobile factory in Pakistan. Energy consumption data, which also included power quality data, were collected with the help of energy analyzers and transmitted to a centralized supervisory control and data acquisition (SCADA) software for data logging and monitoring purposes. This system was developed by combining Modbus with industrial Ethernet to communicate real-time energy consumption data of the factory to multiple local and remote locations. Monitoring and logging the real-time energy consumption data helped the user to find the significant energy losses inside the factory and to implement various energy conservation policies inside the facility, resulting in energy efficiency improvement. The energy consumption results indicate that the proposed system can help achieve an approximately 8% improvement in energy efficiency.